Understanding TraitWeight

TraitWeight is a proprietary algorithm
designed to discover new traits automatically. It compares trait data from your
current traits and segments against all other first and third-party data that
you have access to through Audience Manager. Refer to this section for a
description of the TraitWeight algorithmic discovery process.

The following steps describe the
TraitWeight evaluation process.

Step 1: Build a Baseline for Trait Comparison

To build a baseline, TraitWeight measures all the traits associated with
an audience for a 30, 60, or 90-day interval. Next, it ranks traits according
to their frequency. The frequency count measures commonality. Traits that
appear often are said to exhibit high commonality, an important characteristic
used to set a weighted score when combined with traits discovered in your
selected data sources.

Step 2: Find the Same Traits in the Data Source

After it builds a baseline for comparison, the algorithm looks for
identical traits in your selected data sources. In this step, TraitWeight
performs a frequency count of all discovered traits and compares them to the
baseline. However, unlike the baseline, uncommon traits are ranked higher than
those that appear more often. Rare traits are said to exhibit a high degree of
specificity. TraitWeight assesses combinations of common baseline traits and
uncommon (highly specific) data source traits as more influential or desirable
than traits common to both data sets. In fact, our model recognizes these
large, common traits and does not assign excess priority to data sets with high
correlations. Rare traits get higher priority because they are more likely to
represent new, unique users than traits with high commonality across the board.

Step 3: Assign Weight

In this step, TraitWeight ranks newly discovered traits in order of
influence or desirability. The weight scale is a percentage that runs from 0%
to 100%. Traits ranked closer to 100% means they're more like the audience in
your baseline population. Also, heavily weighted traits are valuable because
they represent new, unique users who may behave similarly to your established,
baseline audience. Remember, TraitWeight considers traits with high commonality
in the baseline and high specificity in the compared data sources to be more
valuable than traits common in each data set.

Step 4: Display and Work with Results

Audience Manager displays your weighted model results in
Trait Builder. When you want to build an algorithmic
trait,
Trait Builder lets you create traits based on the
weighted score generated by the algorithm during a data run. You can use these
results to build accurate traits, or compromise accuracy for reach to help
expand audience size.

Step 5: Re-evaluate the Significance of a Trait Across Processing
Cycles

Periodically, TraitWeight re-evaluates the importance of a trait based
on the size and change in the population of that trait. This happens as the
number of users qualified for that trait increases or decreases over time. This
behavior is most clearly seen in traits that become very large. For example,
suppose the algorithm uses trait A for modeling. As the population of trait A
increases, TraitWeight re-evaluates the importance of that trait and may assign
a lower score or ignore it. In this case, trait A is too common or large to say
anything significant about its population. After TraitWeight reduces the value
of Trait A (or ignores it in the model), the population of the algorithmic
trait decreases. The list of influential traits reflects the evolution of the
baseline population. Use the list of the influential traits to understand why
these changes are occurring.